The work in [1]} is the first attempt to perform a holistic analysis of communication costs in FLchain. Here, MEC severs act as miners to mine blocks containing local updates, by performing a cross-verification on received local updates. To generate blocks, miners cooperate to run a PoW puzzle until they find a nonce value or receive a generated block from other miners. An integrated framework of computation, communication and block generation delays in each communication round is formulated by using an additive white Gaussian noise channel model in the communication phase and a block propagation delay model in the mining phase. The ultimate objective is to find an optimal block generation rate to minimize the average latency incurred by the PoW process while avoiding the possibility of forking caused by incorrect global updating in the block generation phase. Compared to classic FL approaches, such as Vanilla FL [2]}, FLchain achieves a lower system latency with a better learning accuracy in different simulation settings. Another study in [3]} performs a communication cost analysis for an
blackFLchain
in vehicular networks, as indicated in Fig. REF . The use of blockchain potentially helps overcome the centralized malfunctioning problem and enhance the scalability of the intelligent vehicular network by attracting untrustworthy vehicles with a reward mechanism to improve the FL training performance. In this case, each vehicle acts as an FL client to perform local training and exchange with other vehicles via its miner in a blockchain network. More specifically, the latency for FL training and model updating at a vehicle \(n\)  with its associated miner is formulated by jointly considering model computation, model offloading, and blockchain mining as follows:
\(T_n = \frac{\tau _{out}+\mathcal {T}_g}{1-p_{fork}} +\tau _{global}+\tau _{gdn},\) 
